Sandia National Laboratories analyzed methods for controlling distributed energy resources (DER) like photovoltaics (PV) and energy storage to increase the amount of renewable energy that can be integrated onto the distribution grid. The laboratory studied advanced inverter functions and optimal settings, centralized control approaches, and techniques like ramp rate smoothing using distributed storage. Faster communication enables more effective real-time control of DER but also impacts cost and fairness. Sandia is working with partners in Vermont to demonstrate coordinated DER control strategies that provide grid services while managing voltages and curtailing renewables minimally.

1. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin
Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.
Advanced Inverter Functions
and Control of DER
Matthew Reno
Sandia National Laboratories
May 10, 2016
SAND2016-4701 PE

2. Outline
 Advanced Inverter Functions
 Advanced Inverters to Increase Hosting Capacity
 Specific Optimal Advanced Inverter Settings found Using QSTS
 Centralized Control of Advanced PV Inverters
 Optimality vs. Fairness
 Control of DER
 Energy Storage Control for Ramp Rate Smoothing
 Control of DER for Voltage Regulation
 Impact of Communication Delays
 Vermont Regional Partnership for Rebound Mitigation
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3. Hosting Capacity Analysis for 216 feeders
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• Analyses captures a wide range of feeder types, voltages, topologies and controls.
 Detailed analysis of a large number of potential PV
scenarios to determine impact to the distribution system
 Results in finely detailed feeder maps of locational
hosting capacity highlighting the regions for best
placement of additional PV or other DG systems
Advanced Inverters PV Control Control of DER

4. PV Advanced Inverters
 Advanced inverter functions (“smart inverters”) present new
opportunities to increase feeder hosting capacity
Advanced Inverters PV Control Control of DER
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5. Determining Inverter Settings
 The objective is to determine advanced inverter settings to
accommodate more PV without system upgrades
 Not all advanced inverter function settings are beneficial to
the system
 Settings are unique to each feeder and location specific
Advanced Inverters PV Control Control of DER
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6. Derive Custom Settings Using QSTS
 A parametric study is performed using quasi-static time series
(QSTS) analysis for each control type to determine how well
certain measurable network metrics improve as a function of
the control parameters
 Impacts considered for each control:
 Time over-voltage
 Time under-voltage
 Regulator tap changes
 Capacitor switches
 Network losses
 PV power curtailed
 PV vars generated
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Advanced Inverters PV Control Control of DER

7. Derive Settings Using QSTS
 Analysis investigates settings for 6 advanced inverter controls
1. Ramp-Rate Control
2. Fixed Power Factor Control
3. Volt/Watt Control
4. Watt-Triggered Power Factor Control
5. Watt-Priority Volt/Var Control
6. Var-Priority Volt/Var Control
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Advanced Inverters PV Control Control of DER

8. Control of Distributed PV
 Enhanced grid operation and optimized PV penetration
utilizing highly distributed sensor data
 Local, centralized, or distributed control of storage and PV
with advanced inverter capabilities
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Advanced Inverters PV Control Control of DER

9. Control Effectiveness vs. Fairness
 Developed different control methods that achieved curtailment of
less than 3% of kWh produced by PV on the feeder while
maintaining feeder voltages within ANSI standards 98%+ of the
time
 Investigated reactive power control in PV inverters: results show
that real-power curtailment could be avoided over 97% of the time
while maintaining feeder voltages within ANSI standards at all
times.
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Control Type ZCI
Volt/
Watt
Volt/Var
Central
Fair (1m)
Sensitivity-
based (1m)
Violations
Mitigated (%)
100.0 100.0 98.7 99.0 100.0
Power
Curtailed (%)
21.6 5.39 0 5.89 3.99
Curtailment
Deviation (%)
0.75 6.2 0 0.36 8.21
Comparison of the performance of PV inverter control types
Advanced Inverters PV Control Control of DER

10. Distributed Storage For Smoothing PV Ramps
 A control algorithm smooths the variability
of PV power output by using distributed
batteries (300 +systems).
 Both local and central control algorithms
demonstrate that large numbers of highly
distributed current, voltage, and irradiance
sensors can be utilized to control
distributed storage in a more optimal
manner.
 Centralized energy storage control for PV
ramp rate smoothing does require very
fast communication of less than 15 second
update rate.
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Control Name
Regulator Tap
Changes (weekly)
Total Required Storage (kWh)
Basecase with PV and No Batteries 193 -
Local 1 154 662
Local 2 137 2710
Central 1 152 629
Central 2 135 2648
Advanced Inverters PV Control Control of DER

11. Communication Requirements: Smoothing
 Recently funded 3-year project investigating the requirements
for the communication infrastructure for DER control
 Analysis of communication delays, cyber security, and
bandwidth limitations on distributed control
 Using the previous example of dispatching distributed storage
based on PV output for smoothing
1 2 3 4 5 10 15 20 30 45 60 90 120 180 240 300
150
200
250
Communication Resolution (s)
NumberofTapChanges
Start at t=0
Start at 1/2
Start at 1/4
Start at 3/4
Average
No Storage
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Advanced Inverters PV Control Control of DER

12. Control of DER for Voltage Regulation
 Simulation using voltage
measurements at the voltage
regulator to dispatch PV reactive
power to keep the voltage in band
in real-time
 Works for central or distributed
(unbalanced) PV using derived
feeder voltage sensitivities
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Advanced Inverters PV Control Control of DER
OpenDSS Solve
w/Seq. control
Time to
communicate
?
Log data and
go to next
time step
Communication
interval
No
Receive All PPV,
QPV, and VREG
Yes
Any phase
VREG out of
band?
No
Calculate phase
ΔVs to deadband
with gain
Controller
deadband and gain Yes
Calculate
phase ΔQs
SQ 3x3 matrix
Night or
day?
Night
control
enabled
?
Night
Night control
flag
Dispatch
phase ΔQs
Day
OpenDSS
Re-Solve
Set PFs to
unity
Dispatch
phase ΔQs
Yes
No
Historical
Datalog
𝑆𝑆𝑖𝑖 =
Δ𝑉𝑖
Δ𝑄𝑗
∆𝑄 = 𝑆𝑆 −1 ∗ [∆𝑉𝑟𝑟𝑟]

13. Communication Requirements: Voltage
 Impact based on the speed of the feedback control from voltage
measurement at the voltage regulator
 Fast communication mitigates all voltage regulator tap operations
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Advanced Inverters PV Control Control of DER

14. Control of DER: Vermont
 Vermont Regional Partnership Enabling the use of DER
 Development of innovative control strategies (utilizing
aggregates of storage systems along with improved wind and
solar forecasts and controllable loads) that will reduce peak
transmission demand costs while also mitigating solar-
induced feeder voltage variability problems
 Involves control of PV, energy storage, and load management
to provide essential grid services
 There is also a power systems planning component to
develop methods to determine the optimal amount and
placement of DER (PV and storage) on distribution feeders
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Advanced Inverters PV Control Control of DER

15. Summary
 Advanced inverters can provide significant benefits to the
grid, but determining appropriate settings is not always
straightforward
 Fairness is important in DER control design
 Centralized control requires significant and reliable
communication infrastructure, but the necessary update rate
depends on the application.
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16. QUESTIONS?
Sandia National Laboratories
Robert J. Broderick
rbroder@sandia.gov
Matthew J. Reno
mjreno@sandia.gov
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